For example, when I was giving input on hiring our PUM, I identified the following criteria:

Microsoft Experience

patterns & practices Experience

Attract the right talent

Execution

Customer-connection

Engineering Competence

Business Competence

Political Competence

I then assigned a weighting. For example:

Microsoft Experience – (2)

patterns & practices Experience – (3)

Attract the right talent – (3)

Execution – (3)

Customer-connection – (3)

Engineering Competence – (2)

Business Competence – (2)

Political Competence – (2)

I rated the candidate against each criteria and then multiplied by the weighting. This gave me a quick frame to compare different candidates as well as have more meaningful dialogues with others. The actual numbers were less important than testing and clarifying criteria.

This one is a classic pattern used to do everything from judging beauty contests, to selecting tax returns for auditing, and even for determining the sex of a skeleton found at an archeological dig. Neural networks do it too.

I learned about it in engineering school, where it was called the discriminate function.